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<p>Covert hepatic encephalopathy is a highly prevalent complication of liver failure or portosystemic shunting. However, the current diagnostic methods have not been widely adopted in routine clinical practice due to their time-consuming or technically complex nature. This study aimed to develop and evaluate a clinically feasible machine learning model capable of detecting covert hepatic encephalopathy using features extracted from sustained vowel phonation recordings. The XGBoost model achieved the highest AUROC of 81.20 (95% CI 73.03–89.73) and showed reasonable calibration across the full spectrum of predicted probabilities (Hosmer–Lemeshow <i>p</i> = 0.66). The most influential features indicated that patients with covert hepatic encephalopathy tended to have reduced variation in volume, maintaining more constant and harsher vocal quality.</p>

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Prospective evaluation of speech as a digital biomarker for covert hepatic encephalopathy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月22日

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  • Jakub Gazda,
  • Juan Carlos García-Pagán,
  • Sylvia Drazilova,
  • Peter Drotar,
  • Mate Hires,
  • Matej Gazda,
  • Martin Janicko,
  • Anna Baiges,
  • Peter Jarcuska

摘要

Covert hepatic encephalopathy is a highly prevalent complication of liver failure or portosystemic shunting. However, the current diagnostic methods have not been widely adopted in routine clinical practice due to their time-consuming or technically complex nature. This study aimed to develop and evaluate a clinically feasible machine learning model capable of detecting covert hepatic encephalopathy using features extracted from sustained vowel phonation recordings. The XGBoost model achieved the highest AUROC of 81.20 (95% CI 73.03–89.73) and showed reasonable calibration across the full spectrum of predicted probabilities (Hosmer–Lemeshow p = 0.66). The most influential features indicated that patients with covert hepatic encephalopathy tended to have reduced variation in volume, maintaining more constant and harsher vocal quality.